The world of manufacturing, including process engineering, has been under continuous and accelerating pressure to improve quality and reduce costs. This trend shows signs of further accelerating rather than decelerating. From a manufacturing perspective, quality refers to producing parts that 1.) are close to or at engineering design targets, and 2.) exhibit minimal variation. The realm of design engineering has also been under continuous pressure to improve quality and reduce costs. Design engineering must create nominal design targets and establish tolerance limits where it is possible for manufacturing to produce parts that are 1.) on target and 2.) that fall within the design tolerance limits. In-other-words, engineers are tasked not only with designing articles to meet form, fit and function, but with designing them for producibility.
In any manufacturing or other process, there are five fundamental elements (see FIG. 1): 1) the process (A) that makes the product, provides the service or produces the result(s); 2) Inputs into the process (B); 3) Output from the process (C); 4) Process control variables adjusted to influence the process output (D); and, 5) uncontrolled process variables that influence the process (E) (e.g., either uncontrollable variables or variables that are left uncontrolled because of time, cost or other considerations, collectively referred to as “noise.”).
The traditional approach to producing articles, such as parts or other components, that meet design specifications is a logical one based on a search for causation. This approach is based on the principle that, control over the variables that influence a process yields control over the output of that process. In-other-words, if one can control the cause, then one can also control the effect. FIG. 2 illustrates this prior art principle, where an attempt is made to determine the relationships, linkages, or correlations between the control variables and the characteristics of the output (e.g., manufactured parts). Unfortunately, many manufacturing processes act like a black box. It can be difficult in some of these cases to determine the relationship between the process control variables and the resulting article characteristic values. Furthermore, time and economic constraints can make such a determination impractical even when this might be technically possible.
Plastic injection molding is an example of this situation. With at least 22 control variables, even when these control settings have only two levels each (a high and a low temperature, a high and a low pressure, etc.), there are nevertheless over 4 million possible combinations. Indeed, there are billions of possible combinations when three levels (high, medium and low settings) are considered. Furthermore, changes to process variables may have varying effects on the resulting article characteristics; for example, increasing a pressure setting can increase a first article characteristic, decrease a second, and not affect a third. Simple interactions, complex interactions and non-linearities complicate the situation further. In addition, there are usually multiple mold cavities in a single mold. Finally, there are numerous article characteristics (dimensional, performance, or other requirements) that must be met. In light of the preceding, it is often extremely difficult to establish the combination of factors from the large number of part design targets, part tolerance limits, mold design characteristics and injection molding press settings that produces acceptable articles.
Some progress has been made in this regard. Design of Experiments (DOE) methodology greatly reduces the number of experiments that must be conducted to understand the impact of a selected subset of control variables on the resulting output of a process. Unfortunately, even after performing a designed experiment, there are still a large number of control variables that can affect the resulting articles. In any event, extensive measurement of produced parts is still conducted by both the supplier and the OEM customer to ensure that acceptable articles are produced.
In addition, there are two main paths to achieving improved manufacturing quality. The first is to measure the parts after they are produced and then compare the parts to specification requirements (design targets and tolerances). This is an “on-line” process utilizing feedback. The parts are usually measured, to some extent, by both the producer and the customer (OEM, first tier manufacturer, second tier manufacturer, etc.). Measuring the parts, recording and analyzing the data, and reporting the results, however, is a very expensive and resource consuming process.
In their efforts to improve quality, many manufacturers have begun to use the techniques of Statistical Process Control (SPC) and Process Capability studies. Indeed, many customers require their suppliers to perform SPC or other equivalent measurement, recording, analysis and reporting procedures. According to this technique, samples are taken from the production line, measured and then analyzed to see if any abnormal (not normally distributed) patterns or data points emerge. If such abnormal data points are detected, the process is considered “out-of-control” (i.e., failing to yield a consistent predictable output) and production is immediately stopped to fix the process. The measurement data from manufactured parts is analyzed using sophisticated SPC statistical methods and charting tools embodied in specialized computer programs. Since most parts have many different dimensions, measurement and SPC analysis have usually been applied to a large number of part dimensions for each part, increasing the time and expense associated with production. However, SPC is far less expensive in the long run than shipping unacceptable parts and/or having to sort acceptable parts from unacceptable parts.
It has also been difficult for manufacturers (and their customers) to determine 1.) what percentage of the dimensions should be monitored using SPC and 2.) which dimensions should be measured if the full set of dimensions is not monitored. Usually, most, if not all, of the “critical” dimensions called out by the design engineer are measured and analyzed using SPC techniques. However, economic constraints can result in fewer than the desired number of dimensions being measured and analyzed. Guesswork is then frequently involved as to which dimensions to select for SPC or other analysis.
A second path to improving manufacturing quality is by reducing the natural variation in the manufactured articles. The accuracy of maintaining the process control factors can be improved and/or the “noise” factors can be eliminated or minimized. This is an “off-line” process improvement using feed-forward. Reducing natural variation is also an expensive proposition since many relatively small common causes of variation are present. The degree to which the natural variation in the parts produced must be reduced is usually determined through expensive process capability studies, usually conducted on each “critical” dimension.
In light of the foregoing, a need in the art exists for methods, apparatuses and systems facilitating design and manufacturing processes and, more particularly, addressing the problems discussed above. For example, a need in the art exists for methods and systems that allow for reductions in time and cost associated with the measurement, recording, analysis and reporting processes discussed above in connection with, for example, SPC studies, Process Capability studies, shipping inspection and receiving inspection. A need in the art exists for methods to determine how to adjust inputs to a process in order to achieve the desired outputs. A need in the art also exists for methods and systems facilitating a determination of how many article characteristics (e.g., dimensions, performance measures, etc.) should be measured for a given process. Lastly, a need in the art exists for methods and systems that enable an assessment of which article characteristics should be measured for a given process. As discussed in more detail below, embodiments of the present invention substantially fulfill these needs.